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@article{206674,
        author = {Prof. Nithya B P and Prof. Nivin K S and Devika S Nambiar and Pallavi M P and Nandhana M Nambiar and Reshikesh},
        title = {Black Pepper Leaf Disease Detection and Crop Yield Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {137-143},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206674},
        abstract = {Black pepper is an important commercial crop in tropical regions, but its production is often affected by leaf diseases and unstable yield conditions. Traditional methods used for identifying these issues are usually slow and depend heavily on manual observation, which may not always be accurate. This can lead to delayed treatment and reduced productivity. This study presents a smart system that combines image-based disease detection with crop yield prediction. A Convolutional Neural Network (CNN) is used to examine leaf images and identify diseases based on visible features such as color variation, texture changes, and spots. At the same time, a regression-based machine learning model predicts crop yield using soil nutrient values, mainly nitrogen (N), phosphorus (P), and potassium (K).},
        keywords = {Black Pepper, Leaf Disease Detection, Crop Yield Prediction, Machine Learning, CNN, Image Processing},
        month = {July},
        }